Research Article: Visual Data Exploration for Balance Quantification in Real-Time During Exergaming

Date Published: January 30, 2017

Publisher: Public Library of Science

Author(s): Venustiano Soancatl Aguilar, Jasper J. van de Gronde, Claudine J. C. Lamoth, Mike van Diest, Natasha M. Maurits, Jos B. T. M. Roerdink, Jeffrey M Haddad.

http://doi.org/10.1371/journal.pone.0170906

Abstract

Unintentional injuries are among the ten leading causes of death in older adults; falls cause 60% of these deaths. Despite their effectiveness to improve balance and reduce the risk of falls, balance training programs have several drawbacks in practice, such as lack of engaging elements, boring exercises, and the effort and cost of travelling, ultimately resulting in low adherence. Exergames, that is, digital games controlled by body movements, have been proposed as an alternative to improve balance. One of the main challenges for exergames is to automatically quantify balance during game-play in order to adapt the game difficulty according to the skills of the player. Here we perform a multidimensional exploratory data analysis, using visualization techniques, to find useful measures for quantifying balance in real-time. First, we visualize exergaming data, derived from 400 force plate recordings of 40 participants from 20 to 79 years and 10 trials per participant, as heat maps and violin plots to get quick insight into the nature of the data. Second, we extract known and new features from the data, such as instantaneous speed, measures of dispersion, turbulence measures derived from speed, and curvature values. Finally, we analyze and visualize these features using several visualizations such as a heat map, overlapping violin plots, a parallel coordinate plot, a projection of the two first principal components, and a scatter plot matrix. Our visualizations and findings suggest that heat maps and violin plots can provide quick insight and directions for further data exploration. The most promising measures to quantify balance in real-time are speed, curvature and a turbulence measure, because these measures show age-related changes in balance performance. The next step is to apply the present techniques to data of whole body movements as recorded by devices such as Kinect.

Partial Text

Incidence of falls commonly cause serious injuries and loss of independence among the older population. In fact, 20–35% of people more than 65 years old fall each year; this number increases to 32–42% for people over 70 years old [1]. Approximately 20–30% of those people will experience a lack of mobility and independence, thus increasing the risk of death [2, 3]. Furthermore, unintentional injuries are among the ten leading causes of death in older adults and falls cause 60% of these deaths [4]. Although there are many factors that contribute to falls, poor balance is one of the major risk factors for falling due to the natural age-related decline of sensory and neuromuscular control mechanisms that result in impaired postural control [5]. Balance training programs can improve balance ability, thereby reducing the risk of falls and injuries [6]. However, such programs have not been as successful as expected because of several drawbacks, like lack of motivating elements, the effort and cost of travelling, or boring exercises, ultimately resulting in low adherence [7, 8].

For this study we used the data collected in the context of the project Exergaming for balance training of older adults at home [34, 35] of the research center SPRINT of the University Medical Center Groningen (UMCG). In this research center a custom-made ice-skating exergame has been developed for unsupervised training of balance of older adults. Additional information about the exergame and SPRINT can be found in [36].

The main goal of this study was to perform a visual data exploration to find measures that can quantify balance continuously during exergaming. In the absence of a gold standard, we considered a measure to be valuable if it showed time-dependent changes in postural control, and differences between older and younger participants in particular. Our visualizations show that κ, Speed and I′ are the most promising measures because (a) they show differences between older and younger participants, (b) their pairwise scatter plots show clusters of younger and older participants, and (c) they can be estimated during game play and they can be used to provide immediate and appropriate feedback.

Here we have shown how visualization can be used as a way to explore multivariate movement data of young and older adults recorded during exergaming. The properties of heat maps and violin plots can be used to gain quick insight and directions for further exploration. Parallel coordinates, projections, and scatter plot matrices can reveal clusters, patterns, and relationships hidden in the data. Moreover, the creation of such visualizations is straightforward because they are commonly implemented in standard software for statistical analysis.

 

Source:

http://doi.org/10.1371/journal.pone.0170906

 

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